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- W2969259500 abstract "Deep learning (DL) has recently emerged to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MRF) reconstruction. Fed with non-iterated back-projected images, the network is unable to fully resolve spatially-correlated corruptions caused from the undersampling artefacts. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series. Except for training, the rest of the parameter estimation pipeline is dictionary-free. We validate the proposed approach on synthetic and in-vivo datasets." @default.
- W2969259500 created "2019-08-29" @default.
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- W2969259500 date "2019-05-16" @default.
- W2969259500 modified "2023-09-23" @default.
- W2969259500 title "Deep MR Fingerprinting with total-variation and low-rank subspace priors" @default.
- W2969259500 hasPublicationYear "2019" @default.
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